Open physical modeling questions have recently benefited from the accelerating developments of Machine Learning tools. In particular, Deep Learning (DL) carries high hopes of tackling numerous unresolved physical problems. For instance, for the specific field of fluid mechanics, the work of [1]} has been among the precursors for many papers attempting to propose new DL-augmented Reynolds-Averaged Navier-Stokes (RANS) models [2]}, [3]}. To mention a few other applications, DL has also been considered as a tool to produce new Wall Models [4]}, Subgrid-Scale Models [5]}, and Transition Models [6]}. Needless to say that this scientific trend goes well beyond the sole topic of fluid mechanics or even physics and concerns virtually all open modeling problems (for instance, in biotechnology [7]}, solid mechanics [8]}, molecular dynamics [9]}, epidemiology [10]}, etc.).
